Consequently, an emerging thrust in modern ROM closure development efforts is to incorporate machine learning (ML) models [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. The last decade has seen the growth of data-driven modeling technologies (e.g., deep neural networks). So far, a substantial body of closure modeling works has focused on supervised learning [7]}, [2]}, [9]}. A detailed discussion on these models can be found in a recent survey [6]}. In principle, the problem of building a data-driven closure model from the multimodal datasets can be posed as an optimization and ML task. Although supervised learning techniques become more of commodity tools nowadays, reinforcement learning (RL) is relatively an uncharted approach in computational science communities. In contrast to many other data-driven supervised learning based approaches introduced for closures[6]}, this powerful approach can be formulated to tackle with the closure problem in an automated fashion.
